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20132025
most citedDistributed Event-Triggered Control for Global Consensus of Multi-Agent Systems with Input Saturation

161 citations · 490 across the 47 of their papers we have counts for

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Showing 2023Show all

6 papers · 1 filter

math.OC2023

Distributed online constrained convex optimization with event-triggered communication

Kunpeng Zhang, Xinlei Yi, Yuzhe Li +3

This paper focuses on the distributed online convex optimization problem with time-varying inequality constraints over a network of agents, where each agent collaborates with its n…

math.OC2023★ 3 cited

Compressed Gradient Tracking Algorithms for Distributed Nonconvex Optimization

Lei Xu, Xinlei Yi, Guanghui Wen +3

In this paper, we study the distributed nonconvex optimization problem, which aims to minimize the average value of the local nonconvex cost functions using local information excha…

math.OC2023★ 1 cited

Differentially Private and Communication-Efficient Distributed Nonconvex Optimization Algorithms

Antai Xie, Xinlei Yi, Xiaofan Wang +2

This paper studies the privacy-preserving distributed optimization problem under limited communication, where each agent aims to keep its cost function private while minimizing the…

math.OC2023

Asymmetric Feedback Learning in Online Convex Games

Zifan Wang, Xinlei Yi, Yi Shen +2

This paper considers convex games involving multiple agents that aim to minimize their own cost functions using locally available information. A common assumption in the study of s…

math.OC2023★ 1 cited

Distributed Online Convex Optimization with Adversarial Constraints: Reduced Cumulative Constraint Violation Bounds under Slater's Condition

Xinlei Yi, Xiuxian Li, Tao Yang +4

This paper considers distributed online convex optimization with adversarial constraints. In this setting, a network of agents makes decisions at each round, and then only a portio…

math.OC2023

Compressed Differentially Private Distributed Optimization with Linear Convergence

Antai Xie, Xinlei Yi, Xiaofan Wang +2

This paper addresses the problem of differentially private distributed optimization under limited communication, where each agent aims to keep their cost function private while min…